Murtada K. Elbashir

Papers

Total Citations

H-Index

About

Murtada K. Elbashir is a researcher whose work sits at the intersection of computer vision, image processing, and intelligent transportation systems. His most-cited paper, "Distance alert system using Stereo vision and feature extraction" (2017, 4 citations), introduces a practical framework for real-time distance estimation using stereo camera setups and feature extraction techniques. This contribution addresses a critical need in autonomous driving and driver-assistance technologies, where accurate depth perception is essential for collision avoidance. Elbashir’s approach leverages stereo vision to replicate human binocular depth perception, combined with robust feature extraction methods to enhance measurement precision. While his citation count is modest, his work represents a foundational step in applying classical computer vision techniques to safety-critical automotive applications. His research underscores the growing importance of image processing in human-computer interaction and automated systems, offering a cost-effective solution for distance alert systems. For students and researchers exploring the integration of computer vision into real-world safety applications, Elbashir’s work provides a clear, application-driven example of how stereo vision and feature extraction can be harnessed to improve vehicular safety and autonomous navigation.